ML Engineer I

Under guidance from Senior ML Engineers develop ML models that provides accurate results with controls to solve the business problem identified using state of art techniques.

Outcomes:

1. Executes relevant data wrangling activities related to the problem in order to create dataset
2. Conduct ML experiments to understand the feasibility and build baseline models to solve the business problem
3. Fine tune the baseline model for optimum performance
4. Test Models internally per acceptance criteria from business
5. Document relevant Artefacts for communicating with the business
6. Work with data scientists to deploy the models.
7. Work with product teams in planning and execution of new product releases.
8. Set OKRs and success steps for self/ team and provide feedback to goals for team members
9. Work with cross functional teams - business technology and product teams to understand the product vision; building ML solutions that provides value to the product

Measures of Outcomes:

1. Selection of the appropriate approach to the problem
2. Number of successful deployments of the model with optimised accuracy for baseline model
3. Adherence to project schedule / timelines
4. Personal and team achievement of quarterly/yearly objectives (OKR Assignments HIG Stretch goals)

Outputs Expected:

Design to deliver Product Objectives:

1. Design ML solutions which are aligned to and achieve product objectives
2. Define data requirements for the model building and model monitoring; working with product managers to get necessary data

Updated on state of art techniques in the area of AI / ML :

1. Perform necessary research using the latest and state of art techniques to design scalable approaches
2. Explain the relevance of the technologies
   its pros and cons to the product team; enabling accurate design experiences

Skill Examples:

1. Technically strong with the ability to connect the dots
2. Ability to communicate the relevance of technology to the stakeholders in a simple relatable language
3. Curiosity to learn more about new business domains and Technology Innovation
4. An empathetic listener who can give and receive honest thoughtful feedback

Knowledge Examples:

• Expertise in machine learning model building lifecycle
  1. Clear understanding of various ML techniques with appropriate use to business problems
  2. A strong background of statistics and Mathematics
  3. Expertise in one of the domains - Computer Vision Language Understanding or structured data
  4. Experience in executing collaboratively with engineering design user research teams and business stakeholders
  5. Experience with data wrangling techniques preprocessing and post processing requirements for ML solutions
  6. Good knowledge python and deep learning frameworks like Tensorflow Pytorch Caffe
  7. Familiar with the machine learning model testing approaches
  8. A genuine eagerness to work and learn from a diverse and talented team
  
  

  Job Description: We are seeking a visionary AI professional to lead the AIOps team within our Intelligent Automation division. The ideal candidate will bring a minimum of 10 years of experience in AI, with deep expertise in ML, Deep Learning, NLP, and Generative AI. The role requires strong technical leadership, R&D focus, and the ability to mentor and lead a team driving cutting-edge AIOps solutions for IT operations management. Responsibilities: Lead AIOps Research: Drive research and development of AI solutions for AIOps, focusing on predictive maintenance, anomaly detection, incident correlation, and root cause analysis. Project Leadership: Oversee end-to-end delivery of AIOps AI projects, aligning them with organizational goals and client requirements. Technical Expertise: Provide expert-level guidance in ML, DL, NLP, and Generative AI technologies to ensure robust and scalable solutions. Innovation in AIOps: Explore and implement advanced Generative AI, Large Language Models (LLMs), and agentic AI for intelligent automation and IT operations. Team Leadership: Manage, mentor, and inspire a team of AI researchers, data scientists, and ML engineers, fostering a collaborative and innovative environment. Collaboration: Partner with cross-functional teams, including product management, engineering, and business stakeholders, to deliver impactful AIOps solutions. Strategic Vision: Contribute to the development of AIOps strategies and roadmaps, defining milestones and objectives that drive innovation and business value. Qualifications: Bachelor s degree in Computer Science, Engineering, Mathematics, or a related field; advanced degree (Master s or Ph. D. ) preferred. At least 10 years of experience in AI, with a focus on ML, DL, NLP, and Generative AI applications. Proven expertise in AIOps-specific use cases such as predictive maintenance, anomaly detection, and incident management. Proficiency in ML frameworks and libraries like TensorFlow, PyTorch, and Scikit-learn. Strong experience with Generative AI and LLMs such as GPT, BERT, or similar transformer models. Familiarity with MLOps tools, Databricks, and cloud-based AI services from Azure, AWS, and GCP. Demonstrated team leadership experience, with the ability to manage, mentor, and grow technical teams. Excellent problem-solving and analytical skills, with a results-oriented mindset. Exceptional communication skills to effectively collaborate with diverse stakeholders. Ability to operate in a fast-paced, dynamic environment while maintaining a focus on quality and innovation.